Individual DNA sequence variations are known to directly cause specific diseases or conditions, or to predispose certain individuals to specific diseases or conditions. Such variations also modulate the severity or progression of many diseases. Additionally, DNA sequence variations exist between populations. Therefore, determining DNA sequence variations is useful for making accurate diagnoses, for finding suitable therapies, and for understanding the relationship between genome variations and environmental factors in the pathogenesis of diseases and prevalence of conditions.
There are several types of DNA sequence variations. These variations include insertions, deletions, restriction fragment length polymorphisms (“RFLPs”), short tandem repeat polymorphisms (“STRPs”), and single nucleotide polymorphisms (“SNPs”). Of these, SNPs are considered the most useful in studying the relationship between DNA sequence variations and diseases and conditions because they are more common, more stable, and more amenable to being employed in large-scale studies than other sorts of variations.
Currently, a set of over 3 million putative SNPs has been identified in the human genome. It is a current goal of researchers to verify these putative SNPs and associate them with phenotypes and diseases, eventually replacing currently-used RFLP and STRP linkage analysis screening sets. In order to successfully accomplish this goal, it will be necessary for researchers to generate and analyze large amounts of genotypic data.
A number of methods have been developed which can locate or identify SNPs. These methods include dideoxy fingerprinting (ddF), fluorescently labeled ddF, denaturation fingerprinting (DnF1R and DnF2R), single-stranded conformation polymorphism analysis, denaturing gradient gel electrophoresis, heteroduplex analysis, RNase cleavage, chemical cleavage, hybridization sequencing using arrays and direct DNA sequencing.
One method of particular relevance to the present invention employs a pair of fluorescent probes, each probe containing a different dye and specific for a different allele. In this method, the two probes are added to the DNA sample to be tested, and the mixture is amplified using PCR. If the DNA sample is homozygous for the first allele, the first probe's dye will exhibit a high degree of fluorescence and the fluorescence from the second probe's dye will be absent. Conversely, if the DNA sample is homozygous for the second allele, the second probe's dye will exhibit a high degree of fluorescence and the fluorescence from the first probe's dye will be absent. If the DNA sample is heterozygous for both alleles, then both probes should fluoresce equally. A commercial implementation of this method is APPLIED BIOSYSTEMS' TAQMAN platform, which employs APPLIED BIOSYSTEMS' PRISM 7700 and 7900HT SEQUENCE DETECTION SYSTEMS to record the fluorescence of each sample's PCR product.
A typical implementation generates amplification products from a set of a large number of samples at a time, and measures a pair of fluorescence values, one for each dye, from each amplified sample. To classify the samples, it is useful to first plot the fluorescence values of the entire set on a two dimensional graph, and observe that the plotted points tend to cluster into separate groups according to genotype, as illustrated in FIG. 1. In this figure, a human observer can readily discern that the data falls into four groups. The first group, in the lower-left hand corner, represents samples that had no amplification or were a no template control (“NTC”) reaction. The second group, in the lower right hand corner, represents those samples homozygous for Allele 2. The third group, at the top, represents those samples homozygous for Allele 1. Finally, the fourth group, located between the second and third groups, represents the heterozygous samples. This classification is illustrated further in FIG. 2. Although it is relatively easy for human observer to analyze this type of data, it is necessary to develop a fast, reliable, and unsupervised method of computational analysis to produce the level of throughput necessary to analyze the large amounts of genotypic data generated.
Previous methods of computational analysis have employed a family of to algorithms known as clustering algorithms. A typical clustering algorithm receives raw unstructured data and processes it to form groups of data elements that are similar to each other. Clustering algorithms are well known in the field of computer science, and are typically applied in data mining applications. In a data mining application, clustering is used to identify relationships in data collections not readily observable to an expert user due to the volume of information.
A typical clustering algorithm examines the distance between data elements to find a common centroid. The centroid is mean of the value of the data elements belonging to a cluster. Clusters are selected by the algorithm to minimize the distance between the elements contained within it relative to the elements contained in other clusters. Clustering algorithms belong to the greater class of unsupervised machine learning algorithms. Other supervised machine learning algorithms, including decision trees and neural networks, were considered for application to analyzing output from a fluorometric genotyping device. However, all machine learning algorithms considered were determined to be insufficient to analyze this type of data accurately. A thorough review of initial collection of 80 human reviewed outputs revealed characteristics of the data that would not allow standard machine learning algorithms to work with a high degree of accuracy.
It is an object of this invention to provide a fast, accurate, and unsupervised method of classifying genotypic samples based on fluorometric data generated from them.